Inverse modeling of SO<sub>2</sub> and NO<sub><i>x</i></sub> emissions over China using multisensor satellite data – Part 2: Downscaling techniques for air quality analysis and forecasts

Inverse modeling of SO<sub>2</sub> and NO<sub><i>x</i></sub> emissions over China using multisensor satellite data – Part 2: Downscaling techniques for air quality analysis and forecasts

<p><span id="page6652"/>Top-down emission estimates provide valuable up-to-date information on pollution sources; however, the computational effort and spatial resolution of satellite products involved with developing these emissions often require them to be estimated at resolu...

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Main Authors: Y. Wang, J. Wang, M. Zhou, D. K. Henze, C. Ge, W. Wang
Format: Article
Language:English
Published: Copernicus Publications 2020-06-01
Series:Atmospheric Chemistry and Physics
Online Access:https://www.atmos-chem-phys.net/20/6651/2020/acp-20-6651-2020.pdf
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author Y. Wang
J. Wang
J. Wang
M. Zhou
D. K. Henze
C. Ge
C. Ge
W. Wang
author_facet Y. Wang
J. Wang
J. Wang
M. Zhou
D. K. Henze
C. Ge
C. Ge
W. Wang
author_sort Y. Wang
collection DOAJ
description <p><span id="page6652"/>Top-down emission estimates provide valuable up-to-date information on pollution sources; however, the computational effort and spatial resolution of satellite products involved with developing these emissions often require them to be estimated at resolutions that are much coarser than is necessary for regional air quality forecasting. This work thus introduces several approaches to downscaling coarse-resolution (<span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M5" display="inline" overflow="scroll" dspmath="mathml"><mrow><mn mathvariant="normal">2</mn><msup><mi/><mo>∘</mo></msup><mo>×</mo><mn mathvariant="normal">2.5</mn><msup><mi/><mo>∘</mo></msup></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="43pt" height="11pt" class="svg-formula" dspmath="mathimg" md5hash="e3e327827f9255bbd8eb2f675554019e"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="acp-20-6651-2020-ie00001.svg" width="43pt" height="11pt" src="acp-20-6651-2020-ie00001.png"/></svg:svg></span></span>) posterior <span class="inline-formula">SO<sub>2</sub></span> and <span class="inline-formula">NO<sub><i>x</i></sub></span> emissions for improving air quality assessment and forecasts over China in October 2013. As in Part 1 of this study, these <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M8" display="inline" overflow="scroll" dspmath="mathml"><mrow><mn mathvariant="normal">2</mn><msup><mi/><mo>∘</mo></msup><mo>×</mo><mn mathvariant="normal">2.5</mn><msup><mi/><mo>∘</mo></msup></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="43pt" height="11pt" class="svg-formula" dspmath="mathimg" md5hash="e1aaf5b4047def0324c5db88dce1d7d1"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="acp-20-6651-2020-ie00002.svg" width="43pt" height="11pt" src="acp-20-6651-2020-ie00002.png"/></svg:svg></span></span> posterior <span class="inline-formula">SO<sub>2</sub></span> and <span class="inline-formula">NO<sub><i>x</i></sub></span> emission inventories are obtained from GEOS-Chem adjoint modeling with the constraints of OMPS <span class="inline-formula">SO<sub>2</sub></span> and <span class="inline-formula">NO<sub>2</sub></span> products retrieved at <span class="inline-formula">50 km×50 km</span> at nadir and <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M14" display="inline" overflow="scroll" dspmath="mathml"><mrow><mo>∼</mo><mn mathvariant="normal">190</mn><mspace width="0.125em" linebreak="nobreak"/><mrow class="unit"><mi mathvariant="normal">km</mi></mrow><mo>×</mo><mn mathvariant="normal">50</mn><mspace linebreak="nobreak" width="0.125em"/><mrow class="unit"><mi mathvariant="normal">km</mi></mrow></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="86pt" height="10pt" class="svg-formula" dspmath="mathimg" md5hash="be0f90b345790fc3bfd39f735725de9f"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="acp-20-6651-2020-ie00003.svg" width="86pt" height="10pt" src="acp-20-6651-2020-ie00003.png"/></svg:svg></span></span> at the edge of ground track. The prior emission inventory (MIX) and the posterior GEOS-Chem simulations of surface <span class="inline-formula">SO<sub>2</sub></span> and <span class="inline-formula">NO<sub>2</sub></span> concentrations at coarse resolution underestimate observed hot spots, which is called the coarse-grid smearing (CGS) effect. To mitigate the CGS effect, four methods are developed: (a) downscale <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M17" display="inline" overflow="scroll" dspmath="mathml"><mrow><mn mathvariant="normal">2</mn><msup><mi/><mo>∘</mo></msup><mo>×</mo><mn mathvariant="normal">2.5</mn><msup><mi/><mo>∘</mo></msup></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="43pt" height="11pt" class="svg-formula" dspmath="mathimg" md5hash="cb00614ce5e68aee27df96e84dc83c24"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="acp-20-6651-2020-ie00004.svg" width="43pt" height="11pt" src="acp-20-6651-2020-ie00004.png"/></svg:svg></span></span> GEOS-Chem surface <span class="inline-formula">SO<sub>2</sub></span> and <span class="inline-formula">NO<sub>2</sub></span> concentrations to the resolution of <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M20" display="inline" overflow="scroll" dspmath="mathml"><mrow><mn mathvariant="normal">0.25</mn><msup><mi/><mo>∘</mo></msup><mo>×</mo><mn mathvariant="normal">0.3125</mn><msup><mi/><mo>∘</mo></msup></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="76pt" height="11pt" class="svg-formula" dspmath="mathimg" md5hash="6c0122c5f517b964e9293f8b27e553dc"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="acp-20-6651-2020-ie00005.svg" width="76pt" height="11pt" src="acp-20-6651-2020-ie00005.png"/></svg:svg></span></span> through a dynamic downscaling concentration (MIX-DDC) approach, which assumes that the <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M21" display="inline" overflow="scroll" dspmath="mathml"><mrow><mn mathvariant="normal">0.25</mn><msup><mi/><mo>∘</mo></msup><mo>×</mo><mn mathvariant="normal">0.3125</mn><msup><mi/><mo>∘</mo></msup></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="76pt" height="11pt" class="svg-formula" dspmath="mathimg" md5hash="61869edc76f6434e768838cbca6ba70b"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="acp-20-6651-2020-ie00006.svg" width="76pt" height="11pt" src="acp-20-6651-2020-ie00006.png"/></svg:svg></span></span> simulation using the prior MIX emissions has the correct spatial distribution of <span class="inline-formula">SO<sub>2</sub></span> and <span class="inline-formula">NO<sub>2</sub></span> concentrations but a systematic bias; (b) downscale surface <span class="inline-formula">NO<sub>2</sub></span> simulations at <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M25" display="inline" overflow="scroll" dspmath="mathml"><mrow><mn mathvariant="normal">2</mn><msup><mi/><mo>∘</mo></msup><mo>×</mo><mn mathvariant="normal">2.5</mn><msup><mi/><mo>∘</mo></msup></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="43pt" height="11pt" class="svg-formula" dspmath="mathimg" md5hash="b6313970925e1be6cb2b9210f4e4b81a"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="acp-20-6651-2020-ie00007.svg" width="43pt" height="11pt" src="acp-20-6651-2020-ie00007.png"/></svg:svg></span></span> to <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M26" display="inline" overflow="scroll" dspmath="mathml"><mrow><mn mathvariant="normal">0.05</mn><msup><mi/><mo>∘</mo></msup><mo>×</mo><mn mathvariant="normal">0.05</mn><msup><mi/><mo>∘</mo></msup></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="64pt" height="11pt" class="svg-formula" dspmath="mathimg" md5hash="fb5d5ee9be6bf39a026c6c70b10bc67e"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="acp-20-6651-2020-ie00008.svg" width="64pt" height="11pt" src="acp-20-6651-2020-ie00008.png"/></svg:svg></span></span> according to the spatial distribution of Visible Infrared Imaging Radiometer Suite (VIIRS) nighttime light (NL) observations (e.g., NL-DC approach) based on correlation between VIIRS NL intensity with TROPOspheric Monitoring Instrument (TROPOMI) <span class="inline-formula">NO<sub>2</sub></span> observations; (c) downscale posterior emissions (DE) of <span class="inline-formula">SO<sub>2</sub></span> and <span class="inline-formula">NO<sub><i>x</i></sub></span> to <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M30" display="inline" overflow="scroll" dspmath="mathml"><mrow><mn mathvariant="normal">0.25</mn><msup><mi/><mo>∘</mo></msup><mo>×</mo><mn mathvariant="normal">0.3125</mn><msup><mi/><mo>∘</mo></msup></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="76pt" height="11pt" class="svg-formula" dspmath="mathimg" md5hash="a686986a9b4a600f3085804a00300a9a"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="acp-20-6651-2020-ie00009.svg" width="76pt" height="11pt" src="acp-20-6651-2020-ie00009.png"/></svg:svg></span></span> with the assumption that the prior fine-resolution MIX inventory has the correct spatial distribution (e.g., MIX-DE approach); and (d) downscale posterior <span class="inline-formula">NO<sub><i>x</i></sub></span> emissions using VIIRS NL observations (e.g., NL-DE approach). Numerical experiments reveal that (a) using the MIX-DDC approach, posterior <span class="inline-formula">SO<sub>2</sub></span> and <span class="inline-formula">NO<sub>2</sub></span> simulations improve on the corresponding MIX prior simulations with normalized centered root mean square error (NCRMSE) decreases of 63.7&thinsp;% and 30.2&thinsp;%, respectively; (b) the posterior <span class="inline-formula">NO<sub>2</sub></span> simulation has an NCRMSE that is 17.9&thinsp;% smaller than the prior when they are both downscaled through NL-DC, and NL-DC is able to better mitigate the CGS effect than MIX-DDC; (c) the simulation at <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M35" display="inline" overflow="scroll" dspmath="mathml"><mrow><mn mathvariant="normal">0.25</mn><msup><mi/><mo>∘</mo></msup><mo>×</mo><mn mathvariant="normal">0.3125</mn><msup><mi/><mo>∘</mo></msup></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="76pt" height="11pt" class="svg-formula" dspmath="mathimg" md5hash="a02d6da6fe2af4371c56aa09ccd6317e"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="acp-20-6651-2020-ie00010.svg" width="76pt" height="11pt" src="acp-20-6651-2020-ie00010.png"/></svg:svg></span></span> using the MIX-DE approach has NCRMSEs that are 58.8&thinsp;% and 14.7&thinsp;% smaller than the prior <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M36" display="inline" overflow="scroll" dspmath="mathml"><mrow><mn mathvariant="normal">0.25</mn><msup><mi/><mo>∘</mo></msup><mo>×</mo><mn mathvariant="normal">0.3125</mn><msup><mi/><mo>∘</mo></msup></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="76pt" height="11pt" class="svg-formula" dspmath="mathimg" md5hash="21a7b1079ea296fad5da96f5060a3cf9"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="acp-20-6651-2020-ie00011.svg" width="76pt" height="11pt" src="acp-20-6651-2020-ie00011.png"/></svg:svg></span></span> MIX simulation for surface <span class="inline-formula">SO<sub>2</sub></span> and <span class="inline-formula">NO<sub>2</sub></span> concentrations, respectively, but the RMSE from the MIX-DE posterior simulation is slightly larger than that from the MIX-DDC posterior simulation for both <span class="inline-formula">SO<sub>2</sub></span> and <span class="inline-formula">NO<sub>2</sub></span>; (d) the NL-DE posterior <span class="inline-formula">NO<sub>2</sub></span> simulation also improves on the prior MIX simulation at <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M42" display="inline" overflow="scroll" dspmath="mathml"><mrow><mn mathvariant="normal">0.25</mn><msup><mi/><mo>∘</mo></msup><mo>×</mo><mn mathvariant="normal">0.3125</mn><msup><mi/><mo>∘</mo></msup></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="76pt" height="11pt" class="svg-formula" dspmath="mathimg" md5hash="f9bdb3a5c0c170d06a6d15b83863860a"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="acp-20-6651-2020-ie00012.svg" width="76pt" height="11pt" src="acp-20-6651-2020-ie00012.png"/></svg:svg></span></span>, but it is worse than the MIX-DE posterior simulation; (e) in terms of evaluating the downscaled <span class="inline-formula">SO<sub>2</sub></span> and <span class="inline-formula">NO<sub>2</sub></span> simulations simultaneously, using the posterior <span class="inline-formula">SO<sub>2</sub></span> and <span class="inline-formula">NO<sub><i>x</i></sub></span> emissions from joint inverse modeling of both species is better than only using one (<span class="inline-formula">SO<sub>2</sub></span> or <span class="inline-formula">NO<sub><i>x</i></sub></span>) emission from corresponding single-species inverse modeling and is similar to using the posterior emissions of <span class="inline-formula">SO<sub>2</sub></span> and <span class="inline-formula">NO<sub><i>x</i></sub></span> emission inventories respectively from single-species inverse modeling.</p> <p>Forecasts of surface concentrations for November 2013 using the posterior emissions obtained by applying the posterior MIX-DE emissions for October 2013 with the monthly variation information derived from the prior MIX emission inventory show that (a) the improvements of forecasting surface <span class="inline-formula">SO<sub>2</sub></span> concentrations through MIX-DE and MIX-DDC are comparable; (b) for the <span class="inline-formula">NO<sub>2</sub></span> forecast, MIX-DE shows larger improvement than NL-DE and MIX-DDC; (c) NL-DC is able to better decrease the CGS effect than MIX-DE but shows larger NCRMSE; (d) the forecast of surface <span class="inline-formula">O<sub>3</sub></span> concentrations is improved by MIX-DE downscaled posterior <span class="inline-formula">NO<sub><i>x</i></sub></span> emissions. Overall, for practical forecasting of air quality, it is recommended to use satellite-based observation already available from the last month to jointly constrain <span class="inline-formula">SO<sub>2</sub></span> and <span class="inline-formula">NO<sub>2</sub></span> emissions at coarser resolution and then downscale these posterior emissions at finer spatial resolution suitable for regional air quality modeling for the present month.</p>
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spelling doaj.art-af9b7d14af8a4d4d9a30a4c548a35dca2022-12-21T20:30:54ZengCopernicus PublicationsAtmospheric Chemistry and Physics1680-73161680-73242020-06-01206651667010.5194/acp-20-6651-2020Inverse modeling of SO<sub>2</sub> and NO<sub><i>x</i></sub> emissions over China using multisensor satellite data – Part 2: Downscaling techniques for air quality analysis and forecastsY. Wang0J. Wang1J. Wang2M. Zhou3D. K. Henze4C. Ge5C. Ge6W. Wang7Interdisciplinary Graduate Program in Informatics, The University of Iowa, Iowa City, IA 52242, USAInterdisciplinary Graduate Program in Informatics, The University of Iowa, Iowa City, IA 52242, USADepartment of Chemical and Biochemical Engineering, and Center for Global and Regional Environmental Research, The University of Iowa, Iowa City, IA 52242, USAInterdisciplinary Graduate Program in Informatics, The University of Iowa, Iowa City, IA 52242, USADepartment of Mechanical Engineering, University of Colorado, Boulder, CO 80309, USADepartment of Chemical and Biochemical Engineering, and Center for Global and Regional Environmental Research, The University of Iowa, Iowa City, IA 52242, USASouth Coast Air Quality Management District, Diamond Bar, CA 91765, USAChina National Environmental Monitoring Center, Beijing 100012, China<p><span id="page6652"/>Top-down emission estimates provide valuable up-to-date information on pollution sources; however, the computational effort and spatial resolution of satellite products involved with developing these emissions often require them to be estimated at resolutions that are much coarser than is necessary for regional air quality forecasting. This work thus introduces several approaches to downscaling coarse-resolution (<span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M5" display="inline" overflow="scroll" dspmath="mathml"><mrow><mn mathvariant="normal">2</mn><msup><mi/><mo>∘</mo></msup><mo>×</mo><mn mathvariant="normal">2.5</mn><msup><mi/><mo>∘</mo></msup></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="43pt" height="11pt" class="svg-formula" dspmath="mathimg" md5hash="e3e327827f9255bbd8eb2f675554019e"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="acp-20-6651-2020-ie00001.svg" width="43pt" height="11pt" src="acp-20-6651-2020-ie00001.png"/></svg:svg></span></span>) posterior <span class="inline-formula">SO<sub>2</sub></span> and <span class="inline-formula">NO<sub><i>x</i></sub></span> emissions for improving air quality assessment and forecasts over China in October 2013. As in Part 1 of this study, these <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M8" display="inline" overflow="scroll" dspmath="mathml"><mrow><mn mathvariant="normal">2</mn><msup><mi/><mo>∘</mo></msup><mo>×</mo><mn mathvariant="normal">2.5</mn><msup><mi/><mo>∘</mo></msup></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="43pt" height="11pt" class="svg-formula" dspmath="mathimg" md5hash="e1aaf5b4047def0324c5db88dce1d7d1"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="acp-20-6651-2020-ie00002.svg" width="43pt" height="11pt" src="acp-20-6651-2020-ie00002.png"/></svg:svg></span></span> posterior <span class="inline-formula">SO<sub>2</sub></span> and <span class="inline-formula">NO<sub><i>x</i></sub></span> emission inventories are obtained from GEOS-Chem adjoint modeling with the constraints of OMPS <span class="inline-formula">SO<sub>2</sub></span> and <span class="inline-formula">NO<sub>2</sub></span> products retrieved at <span class="inline-formula">50 km×50 km</span> at nadir and <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M14" display="inline" overflow="scroll" dspmath="mathml"><mrow><mo>∼</mo><mn mathvariant="normal">190</mn><mspace width="0.125em" linebreak="nobreak"/><mrow class="unit"><mi mathvariant="normal">km</mi></mrow><mo>×</mo><mn mathvariant="normal">50</mn><mspace linebreak="nobreak" width="0.125em"/><mrow class="unit"><mi mathvariant="normal">km</mi></mrow></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="86pt" height="10pt" class="svg-formula" dspmath="mathimg" md5hash="be0f90b345790fc3bfd39f735725de9f"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="acp-20-6651-2020-ie00003.svg" width="86pt" height="10pt" src="acp-20-6651-2020-ie00003.png"/></svg:svg></span></span> at the edge of ground track. The prior emission inventory (MIX) and the posterior GEOS-Chem simulations of surface <span class="inline-formula">SO<sub>2</sub></span> and <span class="inline-formula">NO<sub>2</sub></span> concentrations at coarse resolution underestimate observed hot spots, which is called the coarse-grid smearing (CGS) effect. To mitigate the CGS effect, four methods are developed: (a) downscale <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M17" display="inline" overflow="scroll" dspmath="mathml"><mrow><mn mathvariant="normal">2</mn><msup><mi/><mo>∘</mo></msup><mo>×</mo><mn mathvariant="normal">2.5</mn><msup><mi/><mo>∘</mo></msup></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="43pt" height="11pt" class="svg-formula" dspmath="mathimg" md5hash="cb00614ce5e68aee27df96e84dc83c24"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="acp-20-6651-2020-ie00004.svg" width="43pt" height="11pt" src="acp-20-6651-2020-ie00004.png"/></svg:svg></span></span> GEOS-Chem surface <span class="inline-formula">SO<sub>2</sub></span> and <span class="inline-formula">NO<sub>2</sub></span> concentrations to the resolution of <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M20" display="inline" overflow="scroll" dspmath="mathml"><mrow><mn mathvariant="normal">0.25</mn><msup><mi/><mo>∘</mo></msup><mo>×</mo><mn mathvariant="normal">0.3125</mn><msup><mi/><mo>∘</mo></msup></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="76pt" height="11pt" class="svg-formula" dspmath="mathimg" md5hash="6c0122c5f517b964e9293f8b27e553dc"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="acp-20-6651-2020-ie00005.svg" width="76pt" height="11pt" src="acp-20-6651-2020-ie00005.png"/></svg:svg></span></span> through a dynamic downscaling concentration (MIX-DDC) approach, which assumes that the <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M21" display="inline" overflow="scroll" dspmath="mathml"><mrow><mn mathvariant="normal">0.25</mn><msup><mi/><mo>∘</mo></msup><mo>×</mo><mn mathvariant="normal">0.3125</mn><msup><mi/><mo>∘</mo></msup></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="76pt" height="11pt" class="svg-formula" dspmath="mathimg" md5hash="61869edc76f6434e768838cbca6ba70b"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="acp-20-6651-2020-ie00006.svg" width="76pt" height="11pt" src="acp-20-6651-2020-ie00006.png"/></svg:svg></span></span> simulation using the prior MIX emissions has the correct spatial distribution of <span class="inline-formula">SO<sub>2</sub></span> and <span class="inline-formula">NO<sub>2</sub></span> concentrations but a systematic bias; (b) downscale surface <span class="inline-formula">NO<sub>2</sub></span> simulations at <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M25" display="inline" overflow="scroll" dspmath="mathml"><mrow><mn mathvariant="normal">2</mn><msup><mi/><mo>∘</mo></msup><mo>×</mo><mn mathvariant="normal">2.5</mn><msup><mi/><mo>∘</mo></msup></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="43pt" height="11pt" class="svg-formula" dspmath="mathimg" md5hash="b6313970925e1be6cb2b9210f4e4b81a"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="acp-20-6651-2020-ie00007.svg" width="43pt" height="11pt" src="acp-20-6651-2020-ie00007.png"/></svg:svg></span></span> to <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M26" display="inline" overflow="scroll" dspmath="mathml"><mrow><mn mathvariant="normal">0.05</mn><msup><mi/><mo>∘</mo></msup><mo>×</mo><mn mathvariant="normal">0.05</mn><msup><mi/><mo>∘</mo></msup></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="64pt" height="11pt" class="svg-formula" dspmath="mathimg" md5hash="fb5d5ee9be6bf39a026c6c70b10bc67e"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="acp-20-6651-2020-ie00008.svg" width="64pt" height="11pt" src="acp-20-6651-2020-ie00008.png"/></svg:svg></span></span> according to the spatial distribution of Visible Infrared Imaging Radiometer Suite (VIIRS) nighttime light (NL) observations (e.g., NL-DC approach) based on correlation between VIIRS NL intensity with TROPOspheric Monitoring Instrument (TROPOMI) <span class="inline-formula">NO<sub>2</sub></span> observations; (c) downscale posterior emissions (DE) of <span class="inline-formula">SO<sub>2</sub></span> and <span class="inline-formula">NO<sub><i>x</i></sub></span> to <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M30" display="inline" overflow="scroll" dspmath="mathml"><mrow><mn mathvariant="normal">0.25</mn><msup><mi/><mo>∘</mo></msup><mo>×</mo><mn mathvariant="normal">0.3125</mn><msup><mi/><mo>∘</mo></msup></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="76pt" height="11pt" class="svg-formula" dspmath="mathimg" md5hash="a686986a9b4a600f3085804a00300a9a"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="acp-20-6651-2020-ie00009.svg" width="76pt" height="11pt" src="acp-20-6651-2020-ie00009.png"/></svg:svg></span></span> with the assumption that the prior fine-resolution MIX inventory has the correct spatial distribution (e.g., MIX-DE approach); and (d) downscale posterior <span class="inline-formula">NO<sub><i>x</i></sub></span> emissions using VIIRS NL observations (e.g., NL-DE approach). Numerical experiments reveal that (a) using the MIX-DDC approach, posterior <span class="inline-formula">SO<sub>2</sub></span> and <span class="inline-formula">NO<sub>2</sub></span> simulations improve on the corresponding MIX prior simulations with normalized centered root mean square error (NCRMSE) decreases of 63.7&thinsp;% and 30.2&thinsp;%, respectively; (b) the posterior <span class="inline-formula">NO<sub>2</sub></span> simulation has an NCRMSE that is 17.9&thinsp;% smaller than the prior when they are both downscaled through NL-DC, and NL-DC is able to better mitigate the CGS effect than MIX-DDC; (c) the simulation at <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M35" display="inline" overflow="scroll" dspmath="mathml"><mrow><mn mathvariant="normal">0.25</mn><msup><mi/><mo>∘</mo></msup><mo>×</mo><mn mathvariant="normal">0.3125</mn><msup><mi/><mo>∘</mo></msup></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="76pt" height="11pt" class="svg-formula" dspmath="mathimg" md5hash="a02d6da6fe2af4371c56aa09ccd6317e"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="acp-20-6651-2020-ie00010.svg" width="76pt" height="11pt" src="acp-20-6651-2020-ie00010.png"/></svg:svg></span></span> using the MIX-DE approach has NCRMSEs that are 58.8&thinsp;% and 14.7&thinsp;% smaller than the prior <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M36" display="inline" overflow="scroll" dspmath="mathml"><mrow><mn mathvariant="normal">0.25</mn><msup><mi/><mo>∘</mo></msup><mo>×</mo><mn mathvariant="normal">0.3125</mn><msup><mi/><mo>∘</mo></msup></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="76pt" height="11pt" class="svg-formula" dspmath="mathimg" md5hash="21a7b1079ea296fad5da96f5060a3cf9"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="acp-20-6651-2020-ie00011.svg" width="76pt" height="11pt" src="acp-20-6651-2020-ie00011.png"/></svg:svg></span></span> MIX simulation for surface <span class="inline-formula">SO<sub>2</sub></span> and <span class="inline-formula">NO<sub>2</sub></span> concentrations, respectively, but the RMSE from the MIX-DE posterior simulation is slightly larger than that from the MIX-DDC posterior simulation for both <span class="inline-formula">SO<sub>2</sub></span> and <span class="inline-formula">NO<sub>2</sub></span>; (d) the NL-DE posterior <span class="inline-formula">NO<sub>2</sub></span> simulation also improves on the prior MIX simulation at <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M42" display="inline" overflow="scroll" dspmath="mathml"><mrow><mn mathvariant="normal">0.25</mn><msup><mi/><mo>∘</mo></msup><mo>×</mo><mn mathvariant="normal">0.3125</mn><msup><mi/><mo>∘</mo></msup></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="76pt" height="11pt" class="svg-formula" dspmath="mathimg" md5hash="f9bdb3a5c0c170d06a6d15b83863860a"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="acp-20-6651-2020-ie00012.svg" width="76pt" height="11pt" src="acp-20-6651-2020-ie00012.png"/></svg:svg></span></span>, but it is worse than the MIX-DE posterior simulation; (e) in terms of evaluating the downscaled <span class="inline-formula">SO<sub>2</sub></span> and <span class="inline-formula">NO<sub>2</sub></span> simulations simultaneously, using the posterior <span class="inline-formula">SO<sub>2</sub></span> and <span class="inline-formula">NO<sub><i>x</i></sub></span> emissions from joint inverse modeling of both species is better than only using one (<span class="inline-formula">SO<sub>2</sub></span> or <span class="inline-formula">NO<sub><i>x</i></sub></span>) emission from corresponding single-species inverse modeling and is similar to using the posterior emissions of <span class="inline-formula">SO<sub>2</sub></span> and <span class="inline-formula">NO<sub><i>x</i></sub></span> emission inventories respectively from single-species inverse modeling.</p> <p>Forecasts of surface concentrations for November 2013 using the posterior emissions obtained by applying the posterior MIX-DE emissions for October 2013 with the monthly variation information derived from the prior MIX emission inventory show that (a) the improvements of forecasting surface <span class="inline-formula">SO<sub>2</sub></span> concentrations through MIX-DE and MIX-DDC are comparable; (b) for the <span class="inline-formula">NO<sub>2</sub></span> forecast, MIX-DE shows larger improvement than NL-DE and MIX-DDC; (c) NL-DC is able to better decrease the CGS effect than MIX-DE but shows larger NCRMSE; (d) the forecast of surface <span class="inline-formula">O<sub>3</sub></span> concentrations is improved by MIX-DE downscaled posterior <span class="inline-formula">NO<sub><i>x</i></sub></span> emissions. Overall, for practical forecasting of air quality, it is recommended to use satellite-based observation already available from the last month to jointly constrain <span class="inline-formula">SO<sub>2</sub></span> and <span class="inline-formula">NO<sub>2</sub></span> emissions at coarser resolution and then downscale these posterior emissions at finer spatial resolution suitable for regional air quality modeling for the present month.</p>https://www.atmos-chem-phys.net/20/6651/2020/acp-20-6651-2020.pdf
spellingShingle Y. Wang
J. Wang
J. Wang
M. Zhou
D. K. Henze
C. Ge
C. Ge
W. Wang
Inverse modeling of SO<sub>2</sub> and NO<sub><i>x</i></sub> emissions over China using multisensor satellite data – Part 2: Downscaling techniques for air quality analysis and forecasts
Atmospheric Chemistry and Physics
title Inverse modeling of SO<sub>2</sub> and NO<sub><i>x</i></sub> emissions over China using multisensor satellite data – Part 2: Downscaling techniques for air quality analysis and forecasts
title_full Inverse modeling of SO<sub>2</sub> and NO<sub><i>x</i></sub> emissions over China using multisensor satellite data – Part 2: Downscaling techniques for air quality analysis and forecasts
title_fullStr Inverse modeling of SO<sub>2</sub> and NO<sub><i>x</i></sub> emissions over China using multisensor satellite data – Part 2: Downscaling techniques for air quality analysis and forecasts
title_full_unstemmed Inverse modeling of SO<sub>2</sub> and NO<sub><i>x</i></sub> emissions over China using multisensor satellite data – Part 2: Downscaling techniques for air quality analysis and forecasts
title_short Inverse modeling of SO<sub>2</sub> and NO<sub><i>x</i></sub> emissions over China using multisensor satellite data – Part 2: Downscaling techniques for air quality analysis and forecasts
title_sort inverse modeling of so sub 2 sub and no sub i x i sub emissions over china using multisensor satellite data part 2 downscaling techniques for air quality analysis and forecasts
url https://www.atmos-chem-phys.net/20/6651/2020/acp-20-6651-2020.pdf
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AT jwang inversemodelingofsosub2subandnosubixisubemissionsoverchinausingmultisensorsatellitedatapart2downscalingtechniquesforairqualityanalysisandforecasts
AT mzhou inversemodelingofsosub2subandnosubixisubemissionsoverchinausingmultisensorsatellitedatapart2downscalingtechniquesforairqualityanalysisandforecasts
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AT wwang inversemodelingofsosub2subandnosubixisubemissionsoverchinausingmultisensorsatellitedatapart2downscalingtechniquesforairqualityanalysisandforecasts

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